A Static Analyzer for Large Safety-Critical Software

We show that abstract interpretation-based static program analysis can be made efficient and precise enough to formally verify a class of properties for a family of large programs with few or no false alarms. This is achieved by refinement of a general purpose static analyzer and later adaptation to particular programs of the family by the end-user through parametrization. This is applied to the proof of soundness of data manipulation operations at the machine level for periodic synchronous safety critical embedded software. The main novelties are the design principle of static analyzers by refinement and adaptation through parametrization, the symbolic manipulation of expressions to improve the precision of abstract transfer functions, the octagon, ellipsoid, and decision tree abstract domains, all with sound handling of rounding errors in floating point computations, widening strategies (with thresholds, delayed) and the automatic determination of the parameters (parametrized packing)

Least Dependent Component Analysis Based on Mutual Information

We propose to use precise estimators of mutual information (MI) to find least dependent components in a linearly mixed signal. On the one hand this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand it has the advantage, compared to other implementations of `independent' component analysis (ICA) some of which are based on crude approximations for MI, that the numerical values of the MI can be used for: (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output, by comparing the pairwise MIs with those of re-mixed components; (iii) clustering the output according to the residual interdependencies. For the MI estimator we use a recently proposed k-nearest neighbor based algorithm. For time sequences we combine this with delay embedding, in order to take into account non-trivial time correlations. After several tests with artificial data, we apply the resulting MILCA (Mutual Information based Least dependent Component Analysis) algorithm to a real-world dataset, the ECG of a pregnant woman. The software implementation of the MILCA algorithm is freely available at http://www.fz-juelich.de/nic/cs/softwareComment: 18 pages, 20 figures, Phys. Rev. E (in press

Targeted prevention of skeletal muscle wasting in cancer cachexia with combinatorial RNAi-based approaches

openCancer cachexia is a wasting syndrome responsible for systemic multi-factorial metabolic dysfunctions leading to severe body weight loss due to excessive muscle and adipose tissue catabolism. The occurrence of cancer cachexia worsens the quality of patients’ lives, reduces the efficacy and tolerance to anti-cancer treatments, and most importantly is directly responsible for up to 30% of cancer-related deaths. Currently, no substantial treatments exist for cancer cachexia, and nutritional support does not completely reverse the condition as well. Skeletal muscle wasting and strength loss are considered among the most deleterious clinical features underlying cancer cachexia and predictors of poor outcomes. Indeed, since in the preclinical models the preservation of skeletal muscle mass is beneficial for survival, independent of tumor growth, it is crucial to uncover signaling pathways underlying muscle atrophy, in order to identify molecular targets that can potentially counteract muscle loss and cachexia onset. Muscle atrophy arises when hyperactivation of proteolysis and organelle degradation exceeds rates of protein synthesis and organelle biogenesis and involves the transcription of genes encoding for rate-limiting enzymes of the degradative systems. Different pathways control the balance of anabolism and catabolism; among all, the most important ones are Akt/FoxOs, IKK-NF-κB, IL6-JAK-Stat3 and the TGF-β/Myostatin-Smad2/3 pathways. In our study, we focused on the activity of FoxOs, a family of transcriptional factors downstream the IGF1/insulin-Akt pathway whose activity controls the expression of crucial genes belonging to both the autophagy/lysosome system and the ubiquitin-proteasome system including Atrogin1/Fbxo32 and MuRF1/Trim63, two E3-ubiquitin ligases strongly upregulated in different catabolic conditions (including cancer cachexia). They are considered master genes of muscle atrophy and they are responsible for myofibrillar protein degradation. In our study, we aim at establishing RNA-based therapeutics methods to prevent muscle wasting and setting up a Spatial Transcriptomics method in muscles to discover underlying gene profiles in tumor-induced muscle loss conditions. Specifically our goals are: i) to generate and validate shRNA constructs against MuRF1 and FoxO1/3 in vitro ; ii) to perform in vivo muscle delivery and validation of shRNA oligos against MuRF1 and FoxO1/3 alone or in combination in the context of cancer-mediated muscle atrophy ; iii) to set up a spatial transcriptomic approach in control and cachectic muscles transfected with shRNA oligos against MuRF1 and FoxO1/3 with the final goal of studying and comparing the transcriptome between these three experimental groups . Our preliminary results show that knocking down of FoxO1/3 and MuRF1 alone in tumor-bearing mice induced partial protection of tumor-induced muscle loss, even if it was not sufficient to cause muscle growth in the control group. However, knocking down the combination of both FoxO1/3 and MuRF1 expressions completely protected cancer-induced muscle loss and was sufficient to mediate a hypertrophic effect in skeletal muscles of the control group. These results suggest that there might be synergistic roles between FoxO1/3 and MuRF1 activities. The spatial transcriptomic approach will allow us to understand the underlying molecular pathways, and genes profiles activity in the course of cancer cachexia, and the rescue condition of cachectic signature with a combinatorial knockdown approach of FoxO1/3 and MuRF1. To conclude, our experimental results and potential future goals aim to create novel combinatorial RNAi-based muscle-targeted therapeutic methods to counteract skeletal muscle wasting in cancer cachexia. The discovery of a novel targeted treatment approach could lead to the amelioration of cancer cachectic patients’ lives and prevent cancer-induced deaths.Cancer cachexia is a wasting syndrome responsible for systemic multi-factorial metabolic dysfunctions leading to severe body weight loss due to excessive muscle and adipose tissue catabolism. The occurrence of cancer cachexia worsens the quality of patients’ lives, reduces the efficacy and tolerance to anti-cancer treatments, and most importantly is directly responsible for up to 30% of cancer-related deaths. Currently, no substantial treatments exist for cancer cachexia, and nutritional support does not completely reverse the condition as well. Skeletal muscle wasting and strength loss are considered among the most deleterious clinical features underlying cancer cachexia and predictors of poor outcomes. Indeed, since in the preclinical models the preservation of skeletal muscle mass is beneficial for survival, independent of tumor growth, it is crucial to uncover signaling pathways underlying muscle atrophy, in order to identify molecular targets that can potentially counteract muscle loss and cachexia onset. Muscle atrophy arises when hyperactivation of proteolysis and organelle degradation exceeds rates of protein synthesis and organelle biogenesis and involves the transcription of genes encoding for rate-limiting enzymes of the degradative systems. Different pathways control the balance of anabolism and catabolism; among all, the most important ones are Akt/FoxOs, IKK-NF-κB, IL6-JAK-Stat3 and the TGF-β/Myostatin-Smad2/3 pathways. In our study, we focused on the activity of FoxOs, a family of transcriptional factors downstream the IGF1/insulin-Akt pathway whose activity controls the expression of crucial genes belonging to both the autophagy/lysosome system and the ubiquitin-proteasome system including Atrogin1/Fbxo32 and MuRF1/Trim63, two E3-ubiquitin ligases strongly upregulated in different catabolic conditions (including cancer cachexia). They are considered master genes of muscle atrophy and they are responsible for myofibrillar protein degradation. In our study, we aim at establishing RNA-based therapeutics methods to prevent muscle wasting and setting up a Spatial Transcriptomics method in muscles to discover underlying gene profiles in tumor-induced muscle loss conditions. Specifically our goals are: i) to generate and validate shRNA constructs against MuRF1 and FoxO1/3 in vitro ; ii) to perform in vivo muscle delivery and validation of shRNA oligos against MuRF1 and FoxO1/3 alone or in combination in the context of cancer-mediated muscle atrophy ; iii) to set up a spatial transcriptomic approach in control and cachectic muscles transfected with shRNA oligos against MuRF1 and FoxO1/3 with the final goal of studying and comparing the transcriptome between these three experimental groups . Our preliminary results show that knocking down of FoxO1/3 and MuRF1 alone in tumor-bearing mice induced partial protection of tumor-induced muscle loss, even if it was not sufficient to cause muscle growth in the control group. However, knocking down the combination of both FoxO1/3 and MuRF1 expressions completely protected cancer-induced muscle loss and was sufficient to mediate a hypertrophic effect in skeletal muscles of the control group. These results suggest that there might be synergistic roles between FoxO1/3 and MuRF1 activities. The spatial transcriptomic approach will allow us to understand the underlying molecular pathways, and genes profiles activity in the course of cancer cachexia, and the rescue condition of cachectic signature with a combinatorial knockdown approach of FoxO1/3 and MuRF1. To conclude, our experimental results and potential future goals aim to create novel combinatorial RNAi-based muscle-targeted therapeutic methods to counteract skeletal muscle wasting in cancer cachexia. The discovery of a novel targeted treatment approach could lead to the amelioration of cancer cachectic patients’ lives and prevent cancer-induced deaths

Leveraging Big Data Analytics for Cultural Teaching Competence in international Chinese Linguistic Learning using Weighted Random Forest Model

The teaching of Chinese language and culture has gained significant importance on the global stage due to China's growing influence in various domains. International Chinese language teachers play a crucial role in promoting cross-cultural understanding and facilitating effective communication between Chinese and non-Chinese speakers. This paper aims to explore the concept of cultural teaching competence for international Chinese language teachers, with a focus on the Chinese national context and the application of a cultural teaching framework supported by big data analytics. The model uses the Integrated Machine Learning Teaching Framework (iMLTF). The model constructs the cultural teaching framework for the evaluation of the International Chinese Language based on Chinese National Context. The iMLTF model uses the Multivariant examination integrated with the Weighted Random Forest model. The simulation analysis expressed that the proposed iMLTF model achieves the higher classification accuracy value of 98% compared with the conventional state-of-art techniques